A Machine Learning Platform for Stock Investment Recommendation Systems

Hernández-Nieves, Elena; Canto, Álvaro Bartolomé del; Chamoso, Pablo; Prieta, Fernando De la; Corchado-Rodríguez, Juan Manuel

doi:10.1007/978-3-030-53036-5_33

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…The technical analysis factors were calculated by utilizing indicators such as Relative Strength Index (RSI), Stochastic Oscillator (STOCH), Simple Moving Average (SMA), Exponential Moving Average (EMA) and others. Finally, their platform provides representations of prices' time-series, tables and charts including financial products' openhigh-low-close values that depend on the aforementioned analyses [22]. Some works integrate sentiment analysis with a SVM-based machine learning method for forecasting stock market trends [23].…”

Section: Ai Era Stock Predictions Solutionsmentioning

confidence: 99%

An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

et al. 2021

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In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors.

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Section: Ai Era Stock Predictions Solutionsmentioning

confidence: 99%

An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

et al. 2021

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“…With the rapid development of information technology, the application of recommendation systems in industry has also gained much importance [6]. Nowadays, the study of recommendation systems has been extended to several aspects, including what to recommend (based on the characteristics of users/items), when to recommend (based on time-aware recommendations), where to recommend (based on geolocation recommendations), who to recommend (based on social network recommendations), and why to recommend (explainable recommendations) [7,8]. Showing why an item is recommended not only helps users understand the rationale for the recommendation, but also helps improve the efciency, transparency, and credibility of the system [9,10].…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Model Based on Improved GRU Network in Big Data Environment

Guo¹,

Zheng

Liu

2023

Journal of Electrical and Computer Engineering

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To address the diversity of user preferences and dynamic changes of interests in the personalized recommendation scenario, a personalized recommendation model based on the improved gated recurrent unit (GRU) network in a big data environment is proposed. First, in order to deal with outliers in sequence recommendation, context awareness sequence recommendation is introduced, and the dynamic changes of users’ interests are modeled by redefining the update gate and the reset gate of the GRU. Then, the duration information about how long users browse each item is processed and transformed to obtain the duration attention factor of each recommended item. And the duration attention factors and the item information are together used as the input of the proposed model for training and prediction. Finally, the auxiliary loss function is introduced to make up for the shortcomings of the traditional negative logarithmic likelihood function, and a super-parameter is applied to combine the auxiliary loss function with the negative logarithmic likelihood function so as to enhance the relationship between the interest representation and the accuracy of recommendation. Experiments show that the root mean square error (RMSE) of the proposed method in the Criteo dataset and MovieLens-1M dataset is 0.7257 and 0.7869, respectively, and the mean absolute error (MAE) is 0.5147 and 0.5893, respectively, which are better than those of the comparison methods. Therefore, the proposed method significantly outperforms the comparison methods in improving the accuracy of personalized recommendation in the system.

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“…T HE emergence of social media has revolutionized communication and information dissemination, facilitating the creation and exchange of user-generated content [1], [2]. In recent years, the development of online platforms has provided users with opportunities to freely share their ideas, emotions, and viewpoints [3], [4]. Notably, investorbased social networks like StockTwits, Etoro, and Guba have emerged as significant platforms that facilitate the exchange and acquisition of investment opinions.…”

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confidence: 99%

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

Wang,

Chen

2024

IEEE Access

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Recommendation systems play a pivotal role in delivering user preference information. However, they often face the challenge of information cocoons due to repeated content delivery, particularly prevalent in stock recommendations that are susceptible to investor sentiment. In response to the information cocoons, we propose the Sentiment and Price Combined Model (SPCM), which leverages sentiment features and price factors to predict stock price movements. This novel framework combines collective sentiment analysis with state-of-the-art BERT transformer models and advanced machine learning techniques. Over a three-year period, we collected 40 million stock comments from the Guba platform, extracting investor sentiment conveyed in text information and investigating the impact of metrics such as homophily on stock recommendations. Experimental results indicate that both the volume of posts and the agreement index affect the effectiveness of investor sentiment, while homophily reduces the accuracy of participants' stock price judgments. The recognition accuracy of the BERT-based sentiment analysis model reaches an impressive 84.12%, and the portfolio constructed by SPCM yields a cumulative return four times that of the industry benchmark. Furthermore, homogeneous quantitative metrics also enhance diversification in stock selection.INDEX TERMS BERT; decision making; homophily; machine learning; stock recommendation I. INTRODUCTION

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A Machine Learning Platform for Stock Investment Recommendation Systems

Cited by 6 publications

References 22 publications

An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

Personalized Recommendation Model Based on Improved GRU Network in Big Data Environment

SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System

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